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NEW QUESTION: 1
You are developing a solution using a Lambda architecture on Microsoft Azure.
The data at test layer must meet the following requirements:
Data storage:
* Serve as a repository (or high volumes of large files in various formats.
* Implement optimized storage for big data analytics workloads.
* Ensure that data can be organized using a hierarchical structure.
Batch processing:
* Use a managed solution for in-memory computation processing.
* Natively support Scala, Python, and R programming languages.
* Provide the ability to resize and terminate the cluster automatically.
Analytical data store:
* Support parallel processing.
* Use columnar storage.
* Support SQL-based languages.
You need to identify the correct technologies to build the Lambda architecture.
Which technologies should you use? To answer, select the appropriate options in the answer area
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Data storage: Azure Data Lake Store
A key mechanism that allows Azure Data Lake Storage Gen2 to provide file system performance at object storage scale and prices is the addition of a hierarchical namespace. This allows the collection of objects/files within an account to be organized into a hierarchy of directories and nested subdirectories in the same way that the file system on your computer is organized. With the hierarchical namespace enabled, a storage account becomes capable of providing the scalability and cost-effectiveness of object storage, with file system semantics that are familiar to analytics engines and frameworks.
Batch processing: HD Insight Spark
Aparch Spark is an open-source, parallel-processing framework that supports in-memory processing to boost the performance of big-data analysis applications.
HDInsight is a managed Hadoop service. Use it deploy and manage Hadoop clusters in Azure. For batch processing, you can use Spark, Hive, Hive LLAP, MapReduce.
Languages: R, Python, Java, Scala, SQL
Analytic data store: SQL Data Warehouse
SQL Data Warehouse is a cloud-based Enterprise Data Warehouse (EDW) that uses Massively Parallel Processing (MPP).
SQL Data Warehouse stores data into relational tables with columnar storage.
References:
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-namespace
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/batch-processing
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-overview-what-is
NEW QUESTION: 2
Escalation rules only run during the business hours with which they are associated?
A. False
B. True
Answer: B
NEW QUESTION: 3
シミュレーション
概要
試験の次のセクションはラボです。このセクションでは、ライブ環境で一連のタスクを実行します。ほとんどの機能はライブ環境と同様に使用できますが、一部の機能（コピーアンドペースト、外部Webサイトへの移動機能など）は設計上不可能です。
スコアリングは、ラボで説明されているタスクを実行した結果に基づいています。つまり、タスクをどのように達成するかは問題ではありません。タスクを正常に実行すると、そのタスクのクレジットを獲得できます。
ラボのタイミングは個別ではなく、この試験には複数のラボが必要な場合があります。各ラボを完了するのに必要な時間を使用できます。ただし、時間を適切に管理して、指定された時間内にラボと試験の他のすべてのセクションを完了することができるようにする必要があります。
ラボ内で[次へ]ボタンをクリックして作業を送信すると、ラボに戻ることはできません。
ラボを開始するには
[次へ]ボタンをクリックして、ラボを開始できます。
最近、Web01という名前の仮想マシンを作成しました。
Web01-Disk1という名前の新しい80 GB標準データディスクをWeb01に接続する必要があります。
Azureポータルから何をすべきですか？
A. データディスクを追加します
手順1. Azureポータルで、左側のメニューから[仮想マシン]を選択します。
ステップ2.リストからWeb01仮想マシンを選択します。
ステップ3. [仮想マシン]ページのEssentialsで、[ディスク]を選択します。
ステップ4. [ディスク]ページで、既存のディスクのリストからWeb01-Disk1を選択します。
ステップ5. [ディスク]ペインで、[+データディスクの追加]をクリックします。
手順6. [名前]のドロップダウンメニューをクリックして、Azureサブスクリプションにアクセスできる既存の管理対象ディスクのリストを表示します。接続する管理対象ディスクWeb01-Disk1を選択します。
B. データディスクを追加します
手順1. Azureポータルで、左側のメニューから[仮想マシン]を選択します。
ステップ2.リストからWeb01仮想マシンを選択します。
ステップ3. [仮想マシン]ページのEssentialsで、[ディスク]を選択します。
ステップ4. [ディスク]ページで、既存のディスクのリストからWeb01-Disk1を選択します。
ステップ5. [ディスク]ペインで、[+データディスクの追加]をクリックします。
手順6. [名前]のドロップダウンメニューをクリックして、Azureサブスクリプションにアクセスできる既存の管理対象ディスクのリストを表示します。接続する管理対象ディスクWeb01-Disk1を選択します。
Answer: A
Explanation:
References:
https://docs.microsoft.com/en-us/azure/virtual-machines/linux/attach-disk-portal